Repetitive TMS of the somatosensory cortex improves writer's cramp and enhances cortical activity.

Since the somatosensory system is believed to be affected in focal dystonia, we focused on the modulation of the primary somatosensory cortex (SI) induced by repetitive transcranial magnetic stimulation (rTMS) in order to improve symptoms of writer's cramp. Patients with writer's cramp (N=9 in the pilot study and N=11 in the advanced study) were treated with 30-minute 1 Hz real- or sham-rTMS of the SI cortex every day for 5 days. Before and after rTMS, 1.5 T fMRI was examined during simple hand movements. While in the pilot study the rTMS coil was navigated over the SI cortex with a maximum of blood oxygenation-level dependent (BOLD) signal induced by passive movement, patients in the advanced study had the coil above the postcentral sulcus. After real-rTMS, 4 pilot study patients and 10 advanced study patients experienced subjective and objective improvement in writing, while only minimal changes were observed after sham-rTMS. Patients involved in the active movement task exhibited a rTMS-induced BOLD signal increase bilaterally in the SI cortex, posterior parietal cortex and in the supplementary motor area (P<0.001 corrected). After sham-rTMS, no BOLD signal changes were observed. In conclusion, 1 Hz rTMS of the SI cortex can improve writer's cramp while increasing the cortical activity in both hemispheres. Handwriting improved in most patients, as well as the subjective benefit, and lasted for 2-3 weeks. The beneficial effects of rTMS paralleled the functional reorganization in the SI cortex and connected areas, reflecting the impact of somatosensory system on active motion control.

Subspace identification methods and fMRI analysis.

The main goal of this paper is to propose application of modern multidimensional systems identification algorithms of the subspace identification theory in the context of fMRI data analysis. The methods originated in 1990s in the field of process control and identification and yield robust linear model parameter estimates for systems with many inputs, outputs and states. Our ultimate goal is to establish an alternative to the DCM analysis procedure which would eliminate its main drawbacks, namely the need to pre-define the models structure. The paper discusses results based on simulated data provided by the DCM simulator in the SPM toolbox. Several scenarios are presented, with varying amount of noise and number of data samples.